A fuzzy based chicken swarm optimization algorithm for efficient fault node detection in Wireless Sensor Networks

Abstract

Abstract Wireless Sensor Networks (WSN) are built with miniature sensor nodes (SN), which are deployed into the geographical location being sensed to monitor environmental conditions, which transfer the sensed physical information to the base station for further processing. The sensor nodes frequently experience node failure as a result of their hostile deployment and resource limitations. In WSN, node failure can cause a number of issues, namely Wireless Sensor Networks topology changes, broken communications links, disconnected portions of the network, and data transmission errors. An important concern of WSN is the detecting, diagnosing and recovering of sensor node failures. In the course of this effort, an effective strategy for sensor node failure detection algorithm using the Poisson Hidden Markov Model (PHMM) and the Fuzzy-based Chicken Swarm Optimization (F-CSO) is proposed for efficient detection of sensor node faults in the WSN. The proposed work offers optimal false alarm, false positive, energy consumption, detection accuracy, network lifetime, and least delay rates. Moreover, the F-CSO provides improved localization to locate the defective sensor nodes that are present in the WSN. The proposed work is implemented in the NS2 simulator with realistic simulation parameters, and the simulation results demonstrate that the proposed work is more effective in terms of 89.5% fault detection accuracy, 19.53% throughput, 8.43% energy consumption with minimum delay and less false positive rate when it is compared with other existing state-of-art systems.

Keywords

• Node fault
• Poisson Hidden Markov model
• Optimization
• Fault detection
• Sensor node